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      Prenatal Amino Acid Supplementation to Improve Fetal Growth: A Systematic Review and Meta-Analysis

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          Abstract

          Aberrant fetal growth remains a leading cause of perinatal morbidity and mortality and is associated with a risk of developing non-communicable diseases later in life. We performed a systematic review and meta-analysis combining human and animal studies to assess whether prenatal amino acid (AA) supplementation could be a promising approach to promote healthy fetal growth. PubMed, Embase, and Cochrane libraries were searched to identify studies orally supplementing the following AA groups during gestation: (1) arginine family, (2) branched chain (BCAA), and (3) methyl donors. The primary outcome was fetal/birth weight. Twenty-two human and 89 animal studies were included in the systematic review. The arginine family and, especially, arginine itself were studied the most. Our meta-analysis showed beneficial effects of arginine and ( N-Carbamyl) glutamate (NCG) but not aspartic acid and citrulline on fetal/birth weight. However, no effects were reported when an isonitrogenous control diet was included. BCAA and methyl donor supplementation did not affect fetal/birth weight. Arginine family supplementation, in particular arginine and NCG, improves fetal growth in complicated pregnancies. BCAA and methyl donor supplementation do not seem to be as promising in targeting fetal growth. Well-controlled research in complicated pregnancies is needed before ruling out AA supplements or preferring arginine above other AAs.

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          Estimating the mean and variance from the median, range, and the size of a sample

          Background Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. Methods In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials. Our estimation is distribution-free, i.e., it makes no assumption on the distribution of the underlying data. Results We found two simple formulas that estimate the mean using the values of the median (m), low and high end of the range (a and b, respectively), and n (the sample size). Using simulations, we show that median can be used to estimate mean when the sample size is larger than 25. For smaller samples our new formula, devised in this paper, should be used. We also estimated the variance of an unknown sample using the median, low and high end of the range, and the sample size. Our estimate is performing as the best estimate in our simulations for very small samples (n ≤ 15). For moderately sized samples (15 70), the formula range/6 gives the best estimator for the standard deviation (variance). We also include an illustrative example of the potential value of our method using reports from the Cochrane review on the role of erythropoietin in anemia due to malignancy. Conclusion Using these formulas, we hope to help meta-analysts use clinical trials in their analysis even when not all of the information is available and/or reported.
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            SYRCLE’s risk of bias tool for animal studies

            Background Systematic Reviews (SRs) of experimental animal studies are not yet common practice, but awareness of the merits of conducting such SRs is steadily increasing. As animal intervention studies differ from randomized clinical trials (RCT) in many aspects, the methodology for SRs of clinical trials needs to be adapted and optimized for animal intervention studies. The Cochrane Collaboration developed a Risk of Bias (RoB) tool to establish consistency and avoid discrepancies in assessing the methodological quality of RCTs. A similar initiative is warranted in the field of animal experimentation. Methods We provide an RoB tool for animal intervention studies (SYRCLE’s RoB tool). This tool is based on the Cochrane RoB tool and has been adjusted for aspects of bias that play a specific role in animal intervention studies. To enhance transparency and applicability, we formulated signalling questions to facilitate judgment. Results The resulting RoB tool for animal studies contains 10 entries. These entries are related to selection bias, performance bias, detection bias, attrition bias, reporting bias and other biases. Half these items are in agreement with the items in the Cochrane RoB tool. Most of the variations between the two tools are due to differences in design between RCTs and animal studies. Shortcomings in, or unfamiliarity with, specific aspects of experimental design of animal studies compared to clinical studies also play a role. Conclusions SYRCLE’s RoB tool is an adapted version of the Cochrane RoB tool. Widespread adoption and implementation of this tool will facilitate and improve critical appraisal of evidence from animal studies. This may subsequently enhance the efficiency of translating animal research into clinical practice and increase awareness of the necessity of improving the methodological quality of animal studies.
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              Birth weight and risk of type 2 diabetes: a systematic review.

              Low birth weight is implicated as a risk factor for type 2 diabetes. However, the strength, consistency, independence, and shape of the association have not been systematically examined. To conduct a quantitative systematic review examining published evidence on the association of birth weight and type 2 diabetes in adults. Relevant studies published by June 2008 were identified through literature searches using EMBASE (from 1980), MEDLINE (from 1950), and Web of Science (from 1980), with a combination of text words and Medical Subject Headings. Studies with either quantitative or qualitative estimates of the association between birth weight and type 2 diabetes were included. Estimates of association (odds ratio [OR] per kilogram of increase in birth weight) were obtained from authors or from published reports in models that allowed the effects of adjustment (for body mass index and socioeconomic status) and the effects of exclusion (for macrosomia and maternal diabetes) to be examined. Estimates were pooled using random-effects models, allowing for the possibility that true associations differed between populations. Of 327 reports identified, 31 were found to be relevant. Data were obtained from 30 of these reports (31 populations; 6090 diabetes cases; 152 084 individuals). Inverse birth weight-type 2 diabetes associations were observed in 23 populations (9 of which were statistically significant) and positive associations were found in 8 (2 of which were statistically significant). Appreciable heterogeneity between populations (I(2) = 66%; 95% confidence interval [CI], 51%-77%) was largely explained by positive associations in 2 native North American populations with high prevalences of maternal diabetes and in 1 other population of young adults. In the remaining 28 populations, the pooled OR of type 2 diabetes, adjusted for age and sex, was 0.75 (95% CI, 0.70-0.81) per kilogram. The shape of the birth weight-type 2 diabetes association was strongly graded, particularly at birth weights of 3 kg or less. Adjustment for current body mass index slightly strengthened the association (OR, 0.76 [95% CI, 0.70-0.82] before adjustment and 0.70 [95% CI, 0.65-0.76] after adjustment). Adjustment for socioeconomic status did not materially affect the association (OR, 0.77 [95% CI, 0.70-0.84] before adjustment and 0.78 [95% CI, 0.72-0.84] after adjustment). There was no strong evidence of publication or small study bias. In most populations studied, birth weight was inversely related to type 2 diabetes risk.
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                Author and article information

                Journal
                Nutrients
                Nutrients
                nutrients
                Nutrients
                MDPI
                2072-6643
                21 August 2020
                September 2020
                : 12
                : 9
                : 2535
                Affiliations
                [1 ]Department for Developmental Origins of Disease (DDOD), Wilhelmina Children’s Hospital University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
                [2 ]Department of Obstetrics, Wilhelmina Children’s Hospital, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; N.D.Paauw-2@ 123456umcutrecht.nl (N.D.P.); A.T.Lely@ 123456umcutrecht.nl (A.T.L.)
                [3 ]Department of Pediatrics, University Medical Center Groningen, 9700 RB Groningen, The Netherlands; a.j.c.tol@ 123456umcg.nl (A.J.C.T.); Eline.VANDERBEEK@ 123456danone.com (E.M.v.d.B.)
                [4 ]Department of Nephrology and Hypertension, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; H.Gremmels@ 123456umcutrecht.nl (H.G.); J.A.Joles@ 123456umcutrecht.nl (J.A.J.)
                [5 ]Systematic Review Center for Laboratory Animal Experimentation (SYRCLE), Department for Health Evidence, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; Kim.Wever@ 123456radboudumc.nl
                [6 ]Danone Nutricia Research, 3584 CT Utrecht, The Netherlands
                Author notes
                [* ]Correspondence: F.Terstappen@ 123456umcutrecht.nl ; Tel.: +31-887577251
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-6587-1320
                https://orcid.org/0000-0001-8632-8135
                https://orcid.org/0000-0002-7818-620X
                https://orcid.org/0000-0003-2565-242X
                Article
                nutrients-12-02535
                10.3390/nu12092535
                7551332
                4980d415-0bd9-434e-97aa-d68c679b6094
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 22 July 2020
                : 19 August 2020
                Categories
                Review

                Nutrition & Dietetics
                amino acids,arginine,birth weight,branched chain amino acid,fetal growth restriction,meta-analysis,methyl donor,pregnancy

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